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The bahavior of Lambda layer of Keras in Tensorflow 2.X was changed from Tensorflow 1.X. In Keras in Tensorflow 1.X, all tf.Variable and tf.get_variable are automatically tracked into the layer.weights via variable creator context so they receive gradient and trainable automatically. Such approach has problem with auto graph compilation that convert Python code into Execution Graph in Tensorflow 2.X so it is removed and now Lambda layer has the code to check for variable creation and raise the error as you see. In short, Lambda layer in Tensorflow 2.X has to be stateless. If you want to create variable, the correct way in Tensorflow 2.X is to subclass layer class and add trainable weight as a class member. Ref: https://stackoverflow.com/questions/65073434/why-keras-lambda-layer-cause-problem-mask-rcnn
Process of upgrading the code to tf 2.x compatible: tf.random_shuffle changed to tf.random.shuffle.
tf.math.log for tf.log in tf 2.0.
tf.math.log for tf.log in tf 2.0.
h5py.File to h5py.save
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The bahavior of Lambda layer of Keras in Tensorflow 2.X was changed from Tensorflow 1.X. In Keras in Tensorflow 1.X, all tf.Variable and tf.get_variable are automatically tracked into the layer.weights via variable creator context so they receive gradient and trainable automatically. Such approach has problem with auto graph compilation that convert Python code into Execution Graph in Tensorflow 2.X so it is removed and now Lambda layer has the code to check for variable creation and raise the error as you see. In short, Lambda layer in Tensorflow 2.X has to be stateless. If you want to create variable, the correct way in Tensorflow 2.X is to subclass layer class and add trainable weight as a class member.
Ref: https://stackoverflow.com/questions/65073434/why-keras-lambda-layer-cause-problem-mask-rcnn